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Source code for catalyst.contrib.utils.dataset

from typing import Callable, Dict, Tuple
from collections import defaultdict
import glob
import itertools
import os

import pandas as pd
from sklearn.model_selection import train_test_split

DictDataset = Dict[str, object]


[docs]def create_dataset( dirs: str, extension: str = None, process_fn: Callable[[str], object] = None, recursive: bool = False, ) -> DictDataset: """ Create dataset (dict like `{key: [values]}`) from vctk-like dataset:: dataset/ cat/ *.ext dog/ *.ext Args: dirs (str): path to dirs, for example /home/user/data/** extension (str): data extension you are looking for process_fn (Callable[[str], object]): function(path_to_file) -> object process function for found files, by default recursive (bool): enables recursive globbing Returns: dict: dataset """ extension = extension or "*" dataset = defaultdict(list) dirs = [os.path.expanduser(k) for k in dirs.strip().split()] dirs = itertools.chain(*(glob.glob(d) for d in dirs)) dirs = [d for d in dirs if os.path.isdir(d)] for d in sorted(dirs): label = os.path.basename(d.rstrip("/")) pathname = d + ("/**/" if recursive else "/") + extension files = glob.iglob(pathname, recursive=recursive) files = sorted(filter(os.path.isfile, files)) if process_fn is None: dataset[label].extend(files) else: dataset[label].extend([process_fn(x) for x in files]) return dataset
[docs]def split_dataset_train_test( dataset: pd.DataFrame, **train_test_split_args ) -> Tuple[DictDataset, DictDataset]: """Split dataset in train and test parts. Args: dataset: dict like dataset **train_test_split_args: test_size : float, int, or None (default is None) If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is automatically set to the complement of the train size. If train size is also None, test size is set to 0.25. train_size : float, int, or None (default is None) If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size. random_state : int or RandomState Pseudo-random number generator state used for random sampling. stratify : array-like or None (default is None) If not None, data is split in a stratified fashion, using this as the class labels. Returns: train and test dicts """ train_dataset = defaultdict(list) test_dataset = defaultdict(list) for key, value in dataset.items(): train_ids, test_ids = train_test_split( range(len(value)), **train_test_split_args ) train_dataset[key].extend([value[i] for i in train_ids]) test_dataset[key].extend([value[i] for i in test_ids]) return train_dataset, test_dataset
[docs]def create_dataframe(dataset: DictDataset, **dataframe_args) -> pd.DataFrame: """Create pd.DataFrame from dict like `{key: [values]}`. Args: dataset: dict like `{key: [values]}` **dataframe_args: index : Index or array-like Index to use for resulting frame. Will default to np.arange(n) if no indexing information part of input data and no index provided columns : Index or array-like Column labels to use for resulting frame. Will default to np.arange(n) if no column labels are provided dtype : dtype, default None Data type to force, otherwise infer Returns: pd.DataFrame: dataframe from giving dataset """ data = [ (key, value) for key, values in dataset.items() for value in values ] df = pd.DataFrame(data, **dataframe_args) return df
__all__ = ["create_dataset", "create_dataframe", "split_dataset_train_test"]